Base Content Slide

"By having all of the pieces in the stack—from the silicon all the way up to the application—we'll be able to deliver systems that run faster, are fault-tolerant, are highly secure—much more secure,

much more performance, much more cost-effective, much easier to use than we ever could have delivered by simply delivering components."

Larry EllisonCEO, Oracle

<Insert Picture Here>

Extreme Performance Data WarehousingÇetin ÖzbütünVice President, Data Warehousing Technologies

The Rise of the Intelligent Economy

“From recession comes an opportunity to reset a number of industry structures…there is an opportunity to infuse

industries with technologies that position them to operate more effectively in the next 50 years.”

Lessons Learned in Building the Intelligent Economy, May 2010

All Businesses Want Better Insight

Industry Typical Questions

Retail What stores should be closed or sold?Which customers will respond to new promotion?

Telecommunications What are the issues effecting churn by region?What is the average revenue per user (ARPU)?

Healthcare What are most common patient service requests?What is average level of clinical supplies on-hand?

Financial Services How will new online services impact deposits?How does average loan compare to last year?

Utilities Who do we target for energy efficiency program?What resources are needed to restore an outage?

Public Sector What is the trend on budget and expenditures?What is most cost-effective way to manage waste?

Less than 500 GB

500 GB - 1 TB

1 - 3 TB

3 - 10 TB

More than 10 TB

21%

20%

21%

19%

17%

5%

12%

18%

25%

34%

In 3 Years Today

Source: TDWI Next Generation Data Warehouse Platforms Report, 2009

Challenge: Much More Data to AnalyzeData Warehouse Size and Growth

Challenge: No Single Source of TruthExpensive Data Warehouse Architecture

ETL

OLAP Data Mining

OLAP Data Mining

ETL

Data Marts

Data Marts

We need platform that supports mixed workloads

Can't support data modeling we need

Current platform is a legacy we must phase out

Poorly suited to real-time or on demand workloads

Cost of scaling up is too expensive

Can't scale to large data volumes

Inadequate data load speed

Can't support advanced analytics

Poor query response

21%

23%

23%

29%

33%

37%

39%

40%

45%

Source: TDWI Next Generation Data Warehouse Platforms Report, 2009

Challenge: User Requirements Not MetHigh Churn in Data Warehouse Platforms

DW Strategy

• Single source of truth

• Extreme performance

• Lower cost of ownership

• Deeper Insight

DW Strategy

• Single source of truth

• Extreme performance

• Lower cost of ownership

• Deeper Insight

A Single Source of Truth? Movielocation see footnote

Consolidate Onto a Single PlatformFaster Performance, Single Source of Truth

Oracle Database 11gOracle Exadata Database Machine

DataMarts

Data Mining

Online Analytics ETL

Oracle Exadata Database MachineFor OLTP, Data Warehousing & Consolidated Workloads

• Improve query performance by 10x– Better insight into customer requirements– Expand revenue opportunities

• Consolidate OLTP and analytic workloads– Lower admin and maintenance costs– Reduce points of failure

• Integrate analytics and data mining– Complex and predictive analytics

• Lower risk– Streamline deployment– One support contact

Oracle Exadata Database Machine FamilyOracle Exadata Database Machine X2-2

Oracle Database Server Grid• 8 2-processor Database Servers

– 96 CPU Cores – 768 GB Memory

Exadata Storage Server Grid• 14 Storage Servers

– 5 TB Smart Flash Cache– 336 TB Disk Storage

Unified Server/Storage Network• 40 Gb/sec Infiniband Links

Available in full, half, quarter racks

Oracle Exadata Database Machine FamilyOracle Exadata Database Machine X2-8

Oracle Database Server Grid• 2 8-processor Database Servers

– 128 CPU Cores – 2 TB Memory– Oracle Linux or Solaris 11 Express

Exadata Storage Server Grid• 14 Storage Servers

– 5 TB Smart Flash Cache– 336 TB Disk Storage

Unified Server/Storage Network• 40 Gb/sec Infiniband Links

Select sum(sales)where salesdate=‘22-Jan-2010’…

Sum

Return entire Sales table

Traditional Query Problem

What Were Yesterday’s

Sales?

• Data is pushed to database server for processing

• I/O rates are limited by speed and number of disk drives

• Network bandwidth is strained, limiting performance and concurrency

Discard most of

sales table

Select sum(sales)where salesdate=‘22-Jan-2010’…

Sum

Return Sales for Jan 22 2010

Exadata Smart ScanImprove Query Performance by 10x or More

What Were Yesterday’s

Sales?

• Off-load data intensive processing to Exadata Storage Server

• Exadata Storage Server only returns relevant rows and columns

• Wide Infiniband connections eliminate network bottlenecks

Exadata Storage IndexTransparent I/O Elimination with No Overhead

• Maintain summary information about table data in memory

• Eliminate disk I/Os if MIN / MAX never match “where” clause

• Completely automatic and transparent

A B C D

1

3

5

5

8

3

Min B = 1Max B =5

Index

Min B = 3Max B =8

Select * from Table where B<2 - Only first set of rows can match

Exadata Hybrid Columnar CompressionReduce Disk Space Requirements

0

10

20

30

40

50

60

70

80

90

100

Da

ta –

Te

rab

yte

s

3x

10x 15x

1.4x

2.5 x

UncompressedData

Data Warehouse Appliances

OLTP Data DW Data

Archive Data

Oracle

Built-in Analytics Secure, Scalable Platform for Advanced Analytics

• Complex and predictive analytics embedded into Oracle Database 11g

• Reduce cost of additional hardware, management resources

• Improve performance by eliminating data movement and duplication

Oracle Data MiningUncover and predict

Oracle OLAPAnalyze and summarize

Exadata Smart Flash CacheExtreme Performance for OLTP Applications

• Automatically caches frequently-accessed ‘hot’ data in flash storage

• Assigns the rest to less expensive disk drives

• Know when to avoid trying to cache data that will never be reused

• Process data at 50GB/sec and up to 1million I/Os per second

Infrequently Used Data

Frequently Used Data

Benefits MultiplyConverting Terabytes to Gigabytes

10 TB of User Data

20 GB of User Data 5 GB of User Data

No IndexesWith Storage Indexes

100 GB of User Data

10 TB of User Data

10 TB of User Data 1 TB of User Data

With Partition PruningWith 10x Compression

With Smart Scan

Sub second “10 TB” Scan

ETL with Oracle

• Fast data loading using DBFS and External Tables

• Fast transforms in Oracle Database 11g via Parallel DML operations

• Best-in-class performance for large batch oriented data loads

Non-Oracle Source

Staging Raw Files

Oracle Source

Data Pump Unload SCP

FTP

BCP Unload

Parallel Loads

Turkcell Runs 10x Faster on Exadata Compresses Data Warehouse by 10x

• Replaced high-end SMP Server and 10 Storage Cabinets• Reduced Data Warehouse from 250TB to 27TB

– Using OLTP & Hybrid Columnar Compression– Ready for future growth where data doubles every year

• Experiencing 10x faster query performance– Delivering over 50,000 reports per month– Average report runs reduced from 27 to 2.5 mins– Up to 400x performance gain on some reports

Softbank Runs 2x–8x Faster on Exadata36 Teradata Racks Replaced by 3 Exadata Racks

Teradata36 Racks

Exadata3 Racks

Workload Management for DWSetting Up a Workload Management System

WorkloadManagement

Define Workloads

Filter Exceptions

Manage Resources

Monitor Workloads

Adjust Plans

Execute Workloads

Monitor Workloads

Adjust Workload

Plans

IORM

RAC OEM

DBRM

Define Workload Plans

Workload Management

Request

Ad-hocWorkload

Each consumer group has:• Resource Allocation (example: 10% of CPU/IO

resources)• Directives (example: 20 active sessions)• Thresholds (example: no jobs longer than 2 min)

RejectDowngrade

Assign

Each request assigned to a consumer group:• OS or DB Username• Application or Module• Action within Module• Administrative

function

Queue

Execute

Each request:• Executes on a RAC Service• Which limits the physical

resources• Allows scalability across racks

Workload Management

Request

Real-TimeETL

Batch ETL

Analytic Reports

OLTP Requests

Ad-hocWorkload

Assign

Reject

Queue

Execute

Downgrade

Execute

Workload Management

Request

Real-TimeETL

Batch ETL

AnalyticReports

OLTP Requests

Ad-hocWorkload

Assign

RejectDowngrade

Queue

Ad-hoc 25%

Analytic Reports

50%

OLTP 5%

Batch 10%

R-T 10%

Queue

Queue

Queue

Queue

Oracle Exadata for Data WarehousingMovie location see

footnote

Oracle Exadata MomentumRapid adoption in all geographies and industries

Oracle Database 11gThe Best Database for Data Warehousing

• World record performance for fast access to information

• Manage growing volumes of information cost-effectively

• Reduce costs through server and data consolidation

Real Application Clusters

Advanced Compression

Partitioning

OLAP

Data Mining

The Concept of PartitioningMaintain Consistent Performance as Database Grows

SALES SALES

Jan Feb

SALES

Jan Feb

Europe

USA

Large Table

• Difficult to Manage

Partition

• Divide and Conquer

• Easier to Manage

• Improve Performance

Composite Partition

• Higher Performance

• Match to business needs

Partition for PerformancePartition Pruning

What was the total sales amount for May 20 and May 21 2010?

Select sum(sales_amount)

From SALES

Where sales_date between

to_date(‘05/20/2010’,’MM/DD/YYYY’)

And

to_date(‘05/22/2010’,’MM/DD/YYYY’);

5/20

5/21

5/22

5/19

Sales Table

• Performs operations only on relevant partitions

• Dramatically reduces amount of data retrieved from disk

• Improves query performance and optimizes resource utilization

Partition to Manage Data Growth Compress Data and Lower Storage Costs

• Distribute partitions across multiple compression tiers

• Free up storage space and execute queries faster

• No changes to existing applications

Active Data

3x OLTP Compression

Read Only Data

10-15x DW Compression

Archive Data

15-50x Archive Compression

In-Memory Parallel ExecutionEfficient use of memory on clustered servers

• Compress more data into available memory on cluster• Intelligent algorithm

– Places table fragments in memory on different nodes• Reduces disk IO and speeds query execution

© 2010 Oracle Corporation

In-Memory Parallel Query in Database Tier

Automated Degree of Parallelism

• Optimizer derives the best Degree of Parallelism

• Based on resource requirements of all concurrent operations

• Less DBA management, better resource utilization

Automatically determine

DOP

Enough parallel servers available

Execute immediately

Queue statements if not enough parallel servers available

When required number of servers are available, execute first statement

8

64 32 16

• Pre-summarized information stored within Oracle Database 11g

• Separate database object, transparent to queries

• Supports sophisticated transparent query rewrite

• Fast incremental refresh of changed data

Summary ManagementImprove Response Time with Materialized Views

Date

Products Channel

SQL QuerySales by

Date

Sales by Product

Sales by Region

Sales by Channel

Region

Materialized ViewsRelational Star

Schema

Query Rewrite

• Exposes Oracle OLAP cubes as relational materialized views

• Provides SQL access to data stored in an OLAP cubes

• Any BI tool or SQL application can leverage OLAP cubes

Region Date

Products Channel

Cube Organized Materialized Views

SQL Query

Automatic Refresh

Query Rewrite

Summaries

DW Strategy

• Single source of truth

• Extreme performance

• Lower cost of ownership

• Deeper Insight

In-database AnalyticsBring Algorithms to the Data, Not Data to the Algorithms

• Analytic computations done in the database– Dimensional analysis– Statistical analysis– Data Mining

• Scalability• Security• Backup & Recovery• Simplicity

OLAP

Data Mining

Statistics

• Multidimensional analytic engine that analyzes summary data

• Offers improved query performance and fast, incremental updates

• Embedded in Oracle Database instance and storage

Oracle OLAPBuilt-in Access to Analytic Calculations

• How do sales in the Western region this quarter compare with sales a year ago?

• What will sales next quarter be?

• What factors can we alter to improve the sales forecast?

Oracle OLAP and OBIEECalculations Computed Faster in OLAP Engine

• Collection of data mining algorithms that solve business problems

• Simplifies development of predictive BI applications

• Embedded in Oracle Database instance and storage

Oracle Data MiningFind Hidden Patterns, Make Predictions

Retail Financial Services

• Customer Segmentation• Response Modeling

• Credit Scoring• Possibility of default

Communications Utilities

• Customer churn• Network intrusion

• Product bundling• Predict power line failure

Healthcare Public Sector

• Patient outcome prediction• Fraud detection

• Tax fraud• Crime analysis

Oracle Data Mining and OBIEEPrediction and Probability Results Integrated in Reports

• Enrich BI with map visualization of Oracle Spatial data

• Enable location analysis in reporting, alerts and notifications

• Use maps to guide data navigation, filtering and drill-down

• Increase ROI from geospatial and non-spatial data

Oracle Spatial and OBIEE

Data Models

Exadata

Business Intelligence

Oracle Exadata Intelligent WarehouseFor Industries

• Combine deep industry knowledge with data warehousing expertise

• Help jump-start design and implementation of data warehouses

• Available for Retail and Communications industries

• Combine deep industry knowledge with data warehousing expertise

• Help jump-start design and implementation of data warehouses

• Optimized for Oracle Database 11g and Oracle Exadata

Reference Data Model

Aggregate Data Model

Relational (STAR) for BIOLAP for Analytical

Derived Data Model

Data Mining/Complex Reports/Query

Base Data Model (3NF)Atomic Level of Transaction Data

Oracle Industry Data Models

Oracle Data WarehousingWhat Customers Think…

Movielocation see footnote

“Oracle Database 11g, along with Oracle Real Application Clusters, Advanced Compression and Partitioning, all lend themselves to delivering highly available, high performance data warehousing.”

Henry Lovoy Data ManagerHealthSouth Corporation

Extreme Performance Data Warehousing Integrated Technology Stack

• Single source of truth

• Easy to deploy and manage

• Extreme performance

• Meets all end user requirements

• Lower cost of ownership

Smart StorageSmart Storage

DatabaseDatabase

Data ModelsData Models

ELT ToolsELT Tools

BI ToolsBI Tools

BI ApplicationsBI Applications

Data Warehouse Reference Architecture

Data Warehouse Reference Architecture

Base data warehouse schemaAtomic-level data, 3nf designSupports general end-user queriesData feeds to all dependent systems

Application-specific performance structuresSummary data / materialized viewsDimensional view of data Supports specific end-users, tools, and applications

Oracle #1 for Data Warehousing

Source: IDC, July 2009 – “Worldwide Data Warehouse Management Tools 2008 Vendor Shares”